Lei Yiming, Zhang Junping, Shan Hongming
Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, 200433 China.
Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China.
Phenomics. 2021 Oct 26;1(6):257-268. doi: 10.1007/s43657-021-00025-y. eCollection 2021 Dec.
Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.
基于低剂量计算机断层扫描（LDCT）图像的肺结节分类由于辐射剂量降低以及其在基于LDCT的肺癌筛查中对肺癌早期诊断的潜力而备受关注。然而，LDCT图像存在严重噪声，这在很大程度上影响了肺结节分类的性能。当前结合去噪和分类任务的方法通常需要相应的正常剂量CT（NDCT）图像作为去噪任务的监督，这在使用LDCT进行临床诊断的背景下是不切实际的。为了在没有NDCT图像的统一框架中联合训练这两个任务，本文引入了一种新颖的自监督方法，称为步长式噪声到邻居（strided Noise2Neighbors或SN2N），用于盲医学图像去噪和肺结节分类，其中监督是从有噪声的输入图像中生成的。更具体地说，所提出的SN2N可以从其邻居构建监督信息用于LDCT去噪，不再需要NDCT图像。所提出的SN2N方法通过使用用于去噪的自监督损失和用于分类的交叉熵损失，实现了LDCT去噪和肺结节分类任务的联合训练。在梅奥LDCT数据集上的广泛实验结果表明，与以配对的NDCT图像作为监督的监督学习方法相比，我们的SN2N取得了有竞争力的性能。此外，我们在LIDC-IDRI数据集上的结果表明，LDCT去噪和肺结节分类的联合训练显著提高了基于LDCT的肺结节分类性能。
